Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

Early detection of ASD Traits in Children using CNN

Abstract: Autism is neurological disorder in which person is affected with communication and interaction abilities. Lacks of social interaction, repetitive behavior, and stable interest are indication of the autistic child. It essential to identify the autism at very is early stage. CNN plays vital role in health care which requires a process that reduces cost and time. The key objective of proposed paper is to implement convolution neural network algorithms and classify autistic and non-autistic child..In this study, CNN is applied for classification of autistic and non-autistic child. The images of children of age 4 to 11 years were used. About 400 images extracted from pre-defined datasets and were used to train the CNN algorithm using the Google colab framework via Python and Open CV libraries. Using cross validation techniques, The CNN was evaluated. In this sense, our proposed model has achieved a high accuracy rate and robustness for prediction of autistic and non-autistic child. Additionally, the proposed algorithm attains a quick response time. Therefore, we could significantly diminish the time of diagnosis by applying the proposed method and facilitate the diagnosis of ASD in lower cost.

Assessment of Elastic-Plastic Fracture Behavior of Cortical Bone Using a Small Punch Testing Technique.

Abstract: The fracture properties of cortical bone are directly coupled to its complex hierarchical structure. The limited availability of bone material from many anatomic locations creates challenges for assessing the effect of bone heterogeneity and anisotropy on fracture properties. The small punch technique was employed to examine the fracture behavior of cortical bone in terms of area under the curve values obtained from load-load point displacement behavior. Fracture toughness of cortical bone was also determined in terms of J-toughness values obtained using a compact tension (CT) test. Area under the curve values obtained from the small punch test were correlated with the J-toughness values of cortical bone. The effects of bone density and compositional parameters on area under the curve and Jtoughness values were also analyzed using linear and multiple regression analysis. Area under the curve and J-toughness values are strongly and positively correlated. Bone density and %mineral content are positively correlated with both area under the curve and J-toughness values. The multiple regression analysis outcomes support these results. Overall, the findings support the hypothesis that area under the curve values obtained from small punch tests can be used to assess the fracture behavior of cortical bone.